DOI 10.1016/j.ejso.2024.108601
Tackling challenges in rare diseases: The ISGACA approach on non-pancreatic cancers in the periampullary region.
Аннотация
OBJECTIVE
Rare but aggressive cancer types like non-pancreatic periampullary cancers pose unique challenges for cancer research due to their low incidence rates and lack of consensus on optimal treatment strategies, therefore necessitating a collaborative approach. The International Study Group on non-pancreatic peri-Ampullary CAncer (ISGACA) aimed to build a collaborative initiative to pool expertise, funding opportunities, and data from over 60 medical centers, in order to improve outcomes for underrepresented patients with rare cancers.
METHODS
The ISGACA approach predefined a stepwise approach including a research scope, establishing a dedicated steering committee, creating a recognizable brand, identifying research gaps, following a well-defined timeline, ensuring robust data collection, addressing legal and ethical considerations, securing financial resources, investing in research ethics training and statistical expertise, raising awareness, creating uniformity, and initiating prospective studies.
RESULTS
Overall, 60 medical centers joined the ISGACA consortium (41 in Europe, 15 in North-America, three in Asia, one in Australia). The database includes 4309 patients. Nine publications and several ongoing studies which in turn allowed for a successful application of research grants. Subsequently, an international consensus meeting established uniform definitions and classifications, and one prospective multicenter international clinical trial has been initiated.
CONCLUSION
By sharing knowledge, expertise, and clinical data, the ISGACA approach has not only gathered sufficient evidence to secure grants and ethical approvals for prospective studies, but also demonstrates options for standardizing patient care and improving outcomes for patients with rare cancers. The ISGACA approach offers a detailed methodology for initiating research on rare cancers and could serve as a replicable model for future research initiatives.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
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